Fast and accurate trajectory prediction is crucial for the development and validation of automated driving systems. Using driver models for prediction is a promising approach to achieve this since they can produce realistic driving behavior at low computational cost. To produce high-quality predictions, the driver model parameters need to be adapted to the current traffic situation and observed driving behavior online. Our work combines data-driven methods with driver models to obtain realistic short-term trajectory predictions. We propose to train machine learning models to predict the driver model parameters that best capture the observed behavior of other vehicles. We use attention-based architectures to process sequential input data and predict the driver model parameters as a weighted sum of prototypes, thus ensuring that the predicted driving model parameters are realistic. Compared to particle filter-based state-of-the-art methods, our approach profits from the rich representational capabilities of learned models and the high online runtime efficiency of driver models. We show that our approach outperforms state-of-the-art methods for online driver model parameter estimation on a real-world traffic dataset.
Data-Driven Online Estimation of Driver Model Parameters for Vehicle Trajectory Prediction
24.09.2024
1890668 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Trajectory prediction for vehicle driver assistance systems
Tema Archiv | 2003
|PROBABILISTIC LONG-TERM VEHICLE TRAJECTORY PREDICTION VIA DRIVER AWARENESS MODEL
British Library Conference Proceedings | 2020
|